CODEWITHSUNDEEP
r
Mono P
Mono P

Reputation: 23

Error in glsEstimate(object, control = control) : computed "gls" fit is singular, rank 19

First time asking in the forums, this time I couldn't find the solutions in other answers.

I'm just starting to learn to use R, so I can't help but think this has a simple solution I'm failing to see.

I'm analyzing the relationship between different insect species (SP) and temperature (T), explanatory variables and the area of the femur of the resulting adult (Femur.area) response variable.

This is my linear model:

ModeloP <- lm(Femur.area ~ T * SP, data=Datos)

No error, but when I want to model variance with gls,

modelo_varPower <- gls(Femur.area ~ T*SP,
                       weights = varPower(),
                       data = Datos
)

I get the following errors...

Error in glsEstimate(object, control = control) : computed "gls" fit is singular, rank 19

The linear model barely passes the Shapiro test of normality, could this be the issue?

Shapiro-Wilk normality test
data:  re
W = 0.98269, p-value = 0.05936

Strangely I've run this model using another explanatory variable and had no errors, all I can read in the forums has to do with multiple samplings along a period of time, and thats not my case. Since the only difference is the response variable I'm uploading and image of how the table looks like in case it helps.

enter image description here

Upvotes: 2

Views: 3862

Answers (1)

Ben Bolker
Ben Bolker

Reputation: 226162

You have some missing cells in your SP:T interaction. lm() tolerates these (if you look at coef(lm(Femur.area~SP*T,data=Datos)) you'll see some NA values for the missing interactions). gls() does not. One way to deal with this is to create an interaction variable and drop the missing levels, then fit the model as (effectively) a one-way rather than a two-way ANOVA. (I called the data dd rather than datos.)

dd3 <- transform(na.omit(dd), SPT=droplevels(interaction(SP,T)))
library(nlme)
gls(Femur.area~SPT,weights=varPower(form=~fitted(.)),data=dd3)

If you want the main effects and the interaction term and the power-law variance that's possible, but it's harder.

Upvotes: 2

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